Analysis and performance of CMA blind deconvolution for image restoration

Pradeepa D. Samarasinghe*, Rodney A. Kennedy

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    Summary In this paper we study the applicability of classical blind deconvolution methods such as constant modulus algorithm (CMA) for blind adaptive image restoration. The requirements such as the source to be white, uniformly distributed and zero mean, which yield satisfactory convergence in the data communication application context, are revisited in the image restoration context, where a linear deblur kernel needs to be blindly adapted to compensate for an unknown image blur kernel with the objective to recover a source ground truth image. Through analysis and performance studies, we show that the performance of CMA is adversely affected by the intrinsic spatial correlation of natural images and by any deviation of their distribution from being platykurtic. We also show that decorrelation techniques designed to overcome spatial correlation cannot be effectively applied to rectify CMA performance for blind adaptive image restoration.

    Original languageEnglish
    Pages (from-to)1135-1151
    Number of pages17
    JournalInternational Journal of Adaptive Control and Signal Processing
    Volume29
    Issue number9
    DOIs
    Publication statusPublished - 1 Sept 2015

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